Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method
(1) UIN Sunan Ampel Surabaya
(2) UIN Sunan Ampel Surabaya
(3) BMKG Perak Maritim II Surabaya
(4) UIN Sunan Ampel Surabaya
(5) UIN Sunan Ampel Surabaya
(6) National Taiwan University of Science and Technology
Abstract
Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions. Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error. Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.
Keywords
Full Text:
PDFReferences
I. Chapsos and S. Hamilton, “Illegal fishing and fisheries crime as a transnational organized crime in Indonesia,” Trends Organ. Crime, vol. 22, no. 3, pp. 255–273, 2019.
S. Darmawan, B. Irawan, C. Setianingsih, and M. A. Murty, “Design of detection device for sea water waves with fuzzy algorithm based on internet of things,” Proc. - 2020 IEEE Int. Conf. Ind. 4.0, Artif. Intell. Commun. Technol. IAICT 2020, pp. 1–6, 2020.
D. C. R. Novitasari, F. Febrianti, and F. Setiawan, “Analisis Kecepatan Angin pada Pasang Surut Air Laut dengan Menggunakan Algoritma Forward-Backward dalam Hidden Markov Modeldi Wilayah Pelabuhan Tanjung Perak Surabaya,” J. Sains Mat. dan Stat., vol. 4, no. 1, pp. 26–35, 2018.
D. D. Kartika, D. C. R. Novitasari, and F. Setiawan, “Prediksi Kecepatan Arus Laut di Perairan Selat Bali Menggunakan Metode Exponential Smoothing Holt-Winters,” MathVisioN, vol. 2, no. 1, pp. 12–17, 2020.
D. A. Adyanti, A. H. Asyhar, D. C. R. Novitasari, A. Lubab, and M. Hafiyusholeh, “Forecasts marine weather on java sea using hybrid methods: Ts-anfis,” in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017.
T. Jansen, “Tinjauan Pengaruh Pasang Surut Terhadap Pola Arus Di Teluk Amurang, Sulawesi Utara,” TEKNO, vol. 16, no. 70, 2018.
I. K. Missa, L. A. S. Lapono, and A. Wahid, “Rancang Bangun Alat Pasang Surut Air Laut Berbasis Arduino Uno dengan Menggunakan Sensor Ultrasonik HC-SR04,” J. Fis. Fis. Sains dan Apl., vol. 3, no. 2, pp. 102–105, 2018.
E. R. Heimisson and J. Avouac, “Analytical prediction of seismicity rate due to tides and other oscillating stresses,” Geophys. Res. Lett., vol. 47, no. 23, 2020.
I. Haqiqi, “Analisis Teknis Dan Ekonomis Kapal Tonda Di Pelabuhan Perikanan Nusantara (PPN) Prigi Trenggalek, Jawa Timur.” Universitas Brawijaya, 2017.
Y. Yang, J. Che, C. Deng, and L. Li, “Sequential grid approach based support vector regression for short-term electric load forecasting,” Appl. Energy, vol. 238, pp. 1010–1021, 2019.
G.-F. Fan, L.-L. Peng, W.-C. Hong, and F. Sun, “Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression,” Neurocomputing, vol. 173, pp. 958–970, 2016.
N. Nikentari, N. Ritha, and T. Haryadi, “Prediksi Pasang Surut Air Laut Menggunakan Jaringan Syaraf Tiruan Backpropagation,” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 7, no. 1, pp. 1–6, 2018.
M. Qin, Z. Li, and Z. Du, “Red tide time series forecasting by combining ARIMA and deep belief network,” Knowledge-Based Syst., vol. 125, pp. 39–52, 2017.
A. Mirarabi, H. R. Nassery, M. Nakhaei, J. Adamowski, A. H. Akbarzadeh, and F. Alijani, “Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems,” Environ. Earth Sci., vol. 78, no. 15, p. 489, 2019.
Z. Ma, C. Ye, and W. Ma, “Support vector regression for predicting building energy consumption in southern China,” Energy Procedia, vol. 158, pp. 3433–3438, 2019.
R. Yulistiani, I. Ramadhan, Q. Said, and M. M. Santoni, “Metode Regresi Linier Berganda dan SVR dalam Menentukan Tingkat Pengaruh Cuaca Terhadap Produktivitas Padi di Indonesia,” SEINASI-KESI, vol. 1, no. 1, pp. 29–34, 2018.
A. Zakrani, A. Najm, and A. Marzak, “Support vector regression based on grid-search method for agile software effort prediction,” in 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), 2018.
Y. Pristyanto and I. Pratama, “Missing Values Estimation on Multivariate Dataset: Comparison of Three Type Methods Approach,” in 2019 International Conference on Information and Communications Technology (ICOIACT), 2019.
V. Demir, M. Zontul, and İ. Yelmen, “Drug Sales Prediction with ACF and PACF Supported ARIMA Method,” in 2020 5th International Conference on Computer Science and Engineering (UBMK), 2020.
X. Yuan, L. Cai-nian, X. Xiao-liang, J. Mei, and Z. Jian-guo, “A two-stage hog feature extraction processor embedded with SVM for pedestrian detection,” in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 3452–3455.
Y. Chen et al., “Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings,” Appl. Energy, vol. 195, pp. 659–670, 2017.
Q. N. Meng and X. Xu, “Price forecasting using an ACO-based support vector regression ensemble in cloud manufacturing,” Comput. Ind. Eng., vol. 125, pp. 171–177, 2018.
C.-H. Huang, F.-H. Yang, and C.-P. Lee, “The strategy of investment in the stock market using modified support vector regression model,” Sci. Iran., vol. 25, no. 3, pp. 1629–1640, 2018.
K. O. Achieng, “Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models,” Comput. Geosci., vol. 133, p. 104320, 2019.
J.-S. Chou and A.-D. Pham, “Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information,” Inf. Sci. (Ny)., vol. 399, pp. 64–80, 2017.
B. Schölkopf and A. J. Smola, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, 2018.
C. López-Martín, M. Azzeh, A. Bou-Nassif, and S. Banitaan, “Upsilon-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects,” in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1377–1382.
G. H. Saputra, A. H. Wigena, and B. Sartono, “Penggunaan Support Vector Regression dalam Pemodelan Indeks Saham Syariah Indonesia dengan Algoritme Grid Search,” Indones. J. Stat. Its Appl., vol. 3, no. 2, pp. 148–160, 2019.
E. M. Priliani, A. T. Putra, and M. A. Muslim, “Forecasting Inflation Rate using Support Vector Regression (SVR) based Weight Attribute Particle Swarm Optimization (WAPSO),” Sci. J. Inf., vol. 5, no. 2, pp. 118–127, 2018.
G. Melki, A. Cano, V. Kecman, and S. Ventura, “Multi-target support vector regression via correlation regressor chains,” Inf. Sci. (Ny)., vol. 415, pp. 53–69, 2017.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.